To overcome this issue, the Fisher kernel based approach proposed by Perronnin et al. [1]} transforms an set of variable-sized independent samples into a fixed size vector representation. The samples are distributed according to a parametric generative model, in this case a Gaussian Mixture Model (GMM) estimated on a training set. A simplified version of Fishers kernels, the VLAD is proposed by Jégou et al. [2]}, [3]}. It encodes the difference from the cluster center in a more direct manner, rather than the frequency assigned to the cluster. It requires less computation than Fisher kernels but can achieve comparable retrieval performance.
